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1. 发明授权

US10586167B2 Regularized model adaptation for in-session recommendations 有权
公开(公告)号：US10586167B2
公开(公告)日：2020-03-10
申请号：US14864499
申请日：2015-09-24
申请人： LinkedIn Corporation
发明人： Xu Miao , Yitong Zhou , Joel D. Young , Lijun Tang , Anmol Bhasin
IPC分类号： G06N20/00 , G06Q10/00 , G06Q30/02 , G06Q10/06 , G06N20/20
摘要： The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.

2. 发明授权

US10380500B2 Version control for asynchronous distributed machine learning 有权
公开(公告)号：US10380500B2
公开(公告)日：2019-08-13
申请号：US14864474
申请日：2015-09-24
申请人： LinkedIn Corporation
发明人： Xu Miao , Yitong Zhou , Joel D. Young , Lijun Tang , Anmol Bhasin
IPC分类号： G06N20/00 , G06F8/71 , G06F8/65
摘要： A system and method for managing asynchronously receiving updates and merging updates into global versions of a statistical model using version control are disclosed. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.

3. 发明申请

US20180285759A1 ONLINE HYPERPARAMETER TUNING IN DISTRIBUTED MACHINE LEARNING 审中-公开
公开(公告)号：US20180285759A1
公开(公告)日：2018-10-04
申请号：US15477782
申请日：2017-04-03
申请人： LinkedIn Corporation
发明人： Ian B. Wood , Xu Miao , Chang-Ming Tsai , Joel D. Young
IPC分类号： G06N7/02 , G06N3/04 , G06F15/18 , G06N3/08 , G06N99/00 , G06F17/30
摘要： The disclosed embodiments provide a system for performing online hyperparameter tuning in distributed machine learning. During operation, the system uses input data for a first set of versions of a statistical model for a set of entities to calculate a batch of performance metrics for the first set of versions. Next, the system applies an optimization technique to the batch to produce updates to a set of hyperparameters for the statistical model. The system then uses the updates to modulate the execution of a second set of versions of the statistical model for the set of entities. When a new entity is added to the set of entities, the system updates the set of hyperparameters with a new dimension for the new entity.

4. 发明申请

US20160041958A1 LEVERAGING ANNOTATION BIAS TO IMPROVE ANNOTATIONS 有权
标题翻译：杠杆倾向偏好来改进附注
公开(公告)号：US20160041958A1
公开(公告)日：2016-02-11
申请号：US14501938
申请日：2014-09-30
申请人： LinkedIn Corporation
发明人： Honglei Zhuang , Joel D. Young
IPC分类号： G06F17/24 , G06K9/62 , G06F17/22
CPC分类号： G06F17/241 , G06F17/16 , G06F17/30598 , G06F17/30864 , G06K9/6277 , G06N7/005 , G06N99/005 , G06Q50/01
摘要： In order to leverage annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system may select a subset of the comments for annotation based on how informative expected annotations for the comments in the subset are for the one or more classifiers and probabilities of occurrence of the expected annotations based on a predetermined annotation probability distribution. Note that the classifier may predict how likely the expected annotations are accurate for the comments in a given subset. Moreover, the predetermined annotation probability distribution may specify the annotation bias. In this way, the system may use the annotation bias to select the subset that is likely to receive expected annotations and, thus, are that are easier to use in training the classifier.
摘要翻译：为了利用通过众包获得的批注释中的注释偏差，关于在社交网络中的用户帖子的一组注释，系统可以基于对子集中的注释的有意义的预期注释来选择用于注释的注释的子集用于基于预定注释概率分布的一个或多个分类器和出现预期注释的概率。请注意，分类器可以预测预期注释对给定子集中的注释的准确性的可能性。此外，预定的注释概率分布可以指定注释偏倚。以这种方式，系统可以使用注释偏差来选择可能接收预期注释的子集，并且因此在训练分类器中更容易使用。

5. 发明授权

US10586169B2 Common feature protocol for collaborative machine learning 有权
公开(公告)号：US10586169B2
公开(公告)日：2020-03-10
申请号：US15046199
申请日：2016-02-17
申请人： LinkedIn Corporation
发明人： David J. Stein , Xu Miao , Lance M. Wall , Joel D. Young , Eric Huang , Songxiang Gu , Da Teng , Chang-Ming Tsai , Sumit Rangwala
IPC分类号： G06N20/00
摘要： The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

6. 发明授权

US09715486B2 Annotation probability distribution based on a factor graph 有权
公开(公告)号：US09715486B2
公开(公告)日：2017-07-25
申请号：US14502319
申请日：2014-09-30
申请人： LinkedIn Corporation
发明人： Honglei Zhuang , Joel D. Young
IPC分类号： G06F17/00 , G06F17/24 , G06F17/16 , G06F17/30 , G06N7/00 , G06N99/00 , G06Q50/00 , G06K9/62
CPC分类号： G06F17/241 , G06F17/16 , G06F17/30598 , G06F17/30864 , G06K9/6277 , G06N7/005 , G06N99/005 , G06Q50/01
摘要： In order to address annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system determines an annotation probability distribution based on a factor-graph model of the batch annotations. In particular, during operation the system computes the factor-graph model that represents relationships between feature vectors that represent the comments and the annotations for the comments. Note that, for a given batch of k comments, the factor-graph model may include a statistically dependent combination of statistically independent models of the interrelationships between the feature vectors and the annotations for the k comments. Then, the system calculates the annotation probability distribution based on model parameters associated with the factor-graph model, a mapping function that maps from the feature vectors to the annotations, and an indicator function that represents the annotations for the comments in the batches.

7. 发明授权

US09665551B2 Leveraging annotation bias to improve annotations 有权
公开(公告)号：US09665551B2
公开(公告)日：2017-05-30
申请号：US14501938
申请日：2014-09-30
申请人： LinkedIn Corporation
发明人： Honglei Zhuang , Joel D. Young
IPC分类号： G06F17/20 , G06F17/24 , G06F17/16 , G06F17/30 , G06N7/00 , G06N99/00 , G06Q50/00 , G06K9/62
CPC分类号： G06F17/241 , G06F17/16 , G06F17/30598 , G06F17/30864 , G06K9/6277 , G06N7/005 , G06N99/005 , G06Q50/01
摘要： In order to leverage annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system may select a subset of the comments for annotation based on how informative expected annotations for the comments in the subset are for the one or more classifiers and probabilities of occurrence of the expected annotations based on a predetermined annotation probability distribution. Note that the classifier may predict how likely the expected annotations are accurate for the comments in a given subset. Moreover, the predetermined annotation probability distribution may specify the annotation bias. In this way, the system may use the annotation bias to select the subset that is likely to receive expected annotations and, thus, are that are easier to use in training the classifier.

8. 发明申请

US20170091652A1 REGULARIZED MODEL ADAPTATION FOR IN-SESSION RECOMMENDATIONS 审中-公开
公开(公告)号：US20170091652A1
公开(公告)日：2017-03-30
申请号：US14864499
申请日：2015-09-24
申请人： LinkedIn Corporation
发明人： Xu Miao , Yitong Zhou , Joel D. Young , Lijun Tang , Anmol Bhasin
IPC分类号： G06N99/00 , G06N7/00
摘要： The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.

9. 发明申请

US20180123918A1 AUTOMATICALLY DETECTING LATENCY BOTTLENECKS IN ASYNCHRONOUS WORKFLOWS 审中-公开
公开(公告)号：US20180123918A1
公开(公告)日：2018-05-03
申请号：US15337567
申请日：2016-10-28
申请人： LinkedIn Corporation
发明人： Antonin Steinhauser , Wing H. Li , Jiayu Gong , Xiaohui Long , Joel D. Young
IPC分类号： H04L12/26 , H04L12/24 , G06F9/48
CPC分类号： G06F9/4831 , G06F9/4881 , H04L41/16 , H04L43/0858 , H04L43/10
摘要： The disclosed embodiments provide a system for processing data. During operation, the system generates, from a set of traces of an asynchronous workflow, a graph-based representation of the asynchronous workflow. Next, the system uses a set of causal relationships in the asynchronous workflow to update the graph-based representation. The system then analyzes the updated graph-based representation to identify a set of high-latency paths in the asynchronous workflow. Finally, the system uses the set of high-latency paths to output an execution profile for the asynchronous workflow, wherein the execution profile includes a subset of tasks associated with the high-latency paths in the asynchronous workflow.

10. 发明申请

US20180121311A1 IDENTIFYING REQUEST-LEVEL CRITICAL PATHS IN MULTI-PHASE PARALLEL TASKS 审中-公开
公开(公告)号：US20180121311A1
公开(公告)日：2018-05-03
申请号：US15337554
申请日：2016-10-28
申请人： LinkedIn Corporation
发明人： Jiayu Gong , Xiaohui Long , Wing H. Li , Joel D. Young
IPC分类号： G06F11/34 , G06F9/48 , G06F11/30
CPC分类号： G06F11/3495 , G06F11/3419 , G06F11/3452 , G06F11/3466
摘要： The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of latencies for a set of requests in a multi-phase parallel task. Next, the system includes the latencies in a graph-based representation of the multi-phase parallel task. The system then analyzes the graph-based representation to identify a set of high-latency paths in the multi-phase parallel task. Finally, the system uses the set of high-latency paths to output an execution profile for the multi-phase parallel task, wherein the execution profile includes a subset of the requests associated with the high-latency paths.

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